Bibliographic record
Abstract
Purpose The authors study how communication agencies became important sites for the rise of measurement expertise in the government of consumer conduct following the development of online consumption. The purpose of this paper is to focus on the processes by which digital measurement developed (within the agencies) as a new legitimate form of expertise, able to produce relevant and detailed knowledge about the government of web users. Design/methodology/approach The authors carried out a field examination in France, predicated on 100 interviews with actors involved in communication consultancy. Drawing on the concepts of governmentality and inter-jurisdictional experimentation, the authors examine how digital measurement expertise acquired legitimacy within agencies. The authors also analyze how contemporary technologies of measurement and surveillance, as operated by in-house digital experts, provide advertising specialists and advertisers with increasingly precise data on consumer conduct and thought. Findings The constitution and legitimization of digital measurement expertise was characterized by experimentation, culminating in the production of persuasive claims of tangibility concerning communication impact, and in relative agreement on the relevance of digital expertise in operating increasingly powerful technologies of measurement and surveillance. Originality/value While the role of experts in promoting and implementing neoliberal governmentality is emphasized in the literature, the study indicates that considerable work is needed to develop and legitimize expertise consequent with neoliberalism. Also, the analysis highlights that the spread of digital measurement expertise and knowledge production in the government of web users constitutes a noteworthy step in the neoliberalization of society. Behind the front of “free” conduct lies an increasingly powerful network of technologies and expertise aimed at rendering consumer conduct knowable and predictable.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.034 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".